A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning
This addresses fall detection for healthcare and safety applications, but it is incremental as it builds on existing ensemble and mobile-cloud approaches.
The paper tackled fall detection by proposing a mobile cloud collaboration system using an ensemble learning method called FEDT, which improved sensitivity and specificity by 1-3% compared to existing methods.
Falls are one of the important causes of accidental or unintentional injury death worldwide. Therefore, this paper presents a reliable fall detection algorithm and a mobile cloud collaboration system for fall detection. The algorithm is an ensemble learning method based on decision tree, named Falldetection Ensemble Decision Tree (FEDT). The mobile cloud collaboration system can be divided into three stages: 1) mobile stage: use a light-weighted threshold method to filter out the activities of daily livings (ADLs), 2) collaboration stage: transmit data to cloud and meanwhile extract features in the cloud, 3) cloud stage: deploy the model trained by FEDT to give the final detection result with the extracted features. Experiments show that the performance of the proposed FEDT outperforms the others' over 1-3% both on sensitivity and specificity, and more importantly, the system can provide reliable fall detection in practical scenario.